the high prevalence of late stage colorectal cancer underscores the need for robust detection systems capable of mitigating its progression during its early stages. While routine colonoscopies have been the industry-s...
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ISBN:
(纸本)9798400716584
the high prevalence of late stage colorectal cancer underscores the need for robust detection systems capable of mitigating its progression during its early stages. While routine colonoscopies have been the industry-standard for identifying signs of early colorectal cancer, it is crucial to uphold several key quality benchmarks to ensure their effectiveness and precision. these quality indices include factors like the scope withdrawal rate and bowel preparation, among others. Our approach leverages on image processing and deep learning to establish a supportive system that highlights areas requiring improvement during scope procedures for clinical practitioners. We demonstrate this via a fine-tuned ResNet-50 architecture to assess bowel preparation yielding 98.5% average accuracy, and a curvature-tracking based approach for colonic anatomical localization for precise monitoring of the withdrawal speed and bowel preparation. We show a pilot iteration of this integrated system on pre-recorded colonoscopy videos, and propose steps for further clinical testing.
Vision transformer models began gaining recognition alongside NLP. However, their performance compared to Convolutional Neural Network (CNN) models in this domain still requires more significant investigations. Hence,...
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ISBN:
(纸本)9798400716584
Vision transformer models began gaining recognition alongside NLP. However, their performance compared to Convolutional Neural Network (CNN) models in this domain still requires more significant investigations. Hence, this article comprehensively analyzes their impact and effectiveness against CNN models in medical imaging. We conducted experiments using ViT, MobileViT, and Swin transformers against a pure CNN ConvNeXt trained on Magnetic Resonance Image (MRI) scans. While our findings show promising advancements in imaging with transformers, we observed challenges in their scalability and deployment due to their cost and complexity. We also noticed that they require more medical data, specifically higher-quality MRI scans, when considering better reliability. Nonetheless, comparing their performance in terms of accuracy, even with such limitations, these visual transformer models have shown better detection and diagnosis of brain tumors in MRI scans compared to pure CNN models selected in this study.
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